How to integrate Didit MCP with Autogen

This guide walks you through connecting Didit to AutoGen using the Composio tool router. By the end, you'll have a working Didit agent that can review pending didit kyc verification sessions, check aml risk flags for users, create identity verification for new customer through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Didit account through Composio's Didit MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Didit is an identity verification and compliance API platform for KYC, KYB, AML, and fraud prevention. It helps teams onboard users faster while reducing risk and meeting compliance needs.

10 Tools

Introduction

This guide walks you through connecting Didit to AutoGen using the Composio tool router. By the end, you'll have a working Didit agent that can review pending didit kyc verification sessions, check aml risk flags for users, create identity verification for new customer through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Didit account through Composio's Didit MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Didit with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Didit
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Didit tools
  • Run a live chat loop where you ask the agent to perform Didit operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

What is the Didit MCP server, and what's possible with it?

The Didit MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Didit account. It provides structured and secure access to identity verification, KYC, KYB, compliance workflows, and billing information, so your agent can create verification sessions, review decisions, inspect user and business profiles, check workflows, and monitor billing balance on your behalf.

  • Verification session creation: Have your agent create a hosted KYC or KYB verification session from a published workflow and provide the verification link to the applicant.
  • Verification review: Direct your agent to retrieve the latest decision and available feature reports for a verification session.
  • User and business oversight: Let the agent fetch individual KYC or KYB profiles, or list users and businesses with their statuses, verification summaries, and session activity.
  • Workflow planning: Instruct your agent to list configured verification workflows and review their publication status, included checks, configuration, and expected pricing.
  • Session and balance monitoring: Have your agent list and filter verification sessions, then check your prepaid credit balance and auto-refill settings before starting billable work.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Didit account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Didit via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Didit connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Didit session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["didit"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Didit tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Didit assistant agent with MCP tools
    agent = AssistantAgent(
        name="didit_assistant",
        description="An AI assistant that helps with Didit operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Didit tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Didit related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Didit tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Didit and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Didit session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["didit"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Didit assistant agent with MCP tools
        agent = AssistantAgent(
            name="didit_assistant",
            description="An AI assistant that helps with Didit operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Didit related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Didit through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Didit, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Didit action and event your agent gets out of the box.

Create Verification Session

Create a hosted KYC or KYB verification session from a published workflow and return the URL to give the end user.

Get Billing Balance

Read the organization's prepaid Didit credit balance and auto-refill settings before starting billable verification work.

Get Business

Fetch one aggregated KYB business profile by its exact vendor_data identifier.

Get User

Fetch one aggregated KYC user profile by its exact vendor_data identifier.

Get Verification Decision

Retrieve the complete current KYC or KYB decision and all feature reports for one verification session.

Get Workflow

Fetch the full configuration and pricing of one workflow version.

List Businesses

List business profiles aggregated from KYB sessions, including status, session counts, registry identifiers, and verification summaries.

List Users

List user profiles aggregated from KYC sessions, including status, session counts, and feature summaries.

List Verification Sessions

List and filter KYC, KYB, or standalone verification sessions.

List Workflows

List configured verification workflow versions, their publication status, feature summary, and expected per-run pricing.

FRAMEWORKS

How to build Didit MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Didit MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Didit MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Didit MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Didit MCP with Claude Cowork

Codex logo

Codex

Use Didit MCP with Codex

OpenClaw logo

OpenClaw

Use Didit MCP with OpenClaw

Hermes logo

Hermes

Use Didit MCP with Hermes

CLI logo

CLI

Use Didit MCP with CLI

Google ADK logo

Google ADK

Use Didit MCP with Google ADK

LangChain logo

LangChain

Use Didit MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Didit MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Didit MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Didit MCP with LlamaIndex

CrewAI logo

CrewAI

Use Didit MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Didit MCP server, the agents and LLMs can only access a fixed set of Didit tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Didit and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Autogen fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Didit tools.

Yes, absolutely. You can configure which Didit scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Didit data and credentials are handled as safely as possible.

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